Search arXivSearch

arXiv · 2608.14582

Enabling Telecommunication Relay Service Research with ACE Omni Platform

Abstract

The Telecommunications Relay Service (TRS) industry is comprised of organizations supported and regulated by the Federal Communications Commission that strive to provide deaf, hard of hearing, or DeafBlind individuals with functionally equivalent telecommunication services. Research that assesses current and proposed TRS technologies is used to help close functional equivalence gaps and create recommendations for regulation. TRS researchers spend a considerable amount of time and effort developing experimental environments, which has limited the field's ability to produce empirical studies. In response to this challenge, the MITRE Corporation has developed a telecommunications research platform called Accessible Communications for Everyone (ACE) Omni. This platform enables researchers to efficiently set up TRS experimental environments, emulate functionality of current TRS technologies, and test new technology solutions. Various design processes, information gathering activities, and the development of personas, research workflows, and functional requirements were leveraged in the design of ACE Omni. A preliminary validation study was conducted via in-lab piloting activities and in vivo to collect real-world TRS user data. Challenges during validation were addressed by making ACE Omni and/or study protocol modifications, and lessons learned are discussed. ACE Omni has the potential to reduce the time and financial costs associated with setting up and running TRS studies, which can promote more TRS research and enable improved service, telecommunication experiences, and outcomes for the community of TRS users.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Harrison Bourikas, Eric Kosinski, Karina Roundtree, Ronna ten Brink, Mike Woodman, Vincent Ybarra. 2026-06-16. Enabling Telecommunication Relay Service Research with ACE Omni Platform. https://arxiv.org/abs/2608.14582

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Semord: Learned Semantic-Preserving Placement and Low-Fanout Routing for Distributed Vector Search

Vector databases are increasingly deployed in distributed settings where different users, sites, or domains maintain vector data. Existing vector databases rely on a coordinator to record which shards store which parts of the vector space and to route each query to those shards. In a decentralized setting, peers may join, leave, or move data without a trusted node tracking every change, and outdated routing information can therefore send queries to the wrong peers or require contacting many peers, reducing vector retrieval recall and increasing network latency. We present Semord, a decentralized vector search overlay system that achieves high recall by routing each ANN query to a small set of relevant peers, without relying on a centralized coordinator. Semord addresses this problem by making semantic locality routable: 1) We propose VHash to place semantically related vectors near each other in the overlay key space while avoiding load imbalance, so that each query only needs to contact a small neighborhood of peers for distributed local ANN ranking. 2) We design VecDHT, a communication protocol that maintains decentralized routing, region metadata, churn resilience, and VHash updates under membership and workload changes. Our extensive experiments on a real testbed show that Semord improves recall by more than 15% and reduces contacted peers by over 60% compared with decentralized baselines. Semord also approaches the recall and latency of a centralized oracle baseline while reducing peak peer-local ANN index memory by more than 2X. Controlled large-scale simulations further show that Semord scales across real-world embedding workloads and remains robust under churn for scoped vector retrieval as a decentralized overlay.

cs.NI

Lizard: Bandwidth-Adaptive Real-Time Video Analytics through Content-Aware Packet Discarding at Last-Mile Edge Routers

The timeliness and accuracy of edge-based video analytics can be hindered by drastic reductions in available bandwidth (ABW) at last-mile edge routers, causing prolonged queuing delays. This work proposes Lizard, a system that leverages video-content-aware packet discarding to mitigate the negative effects of drastic ABW degradation that may frequently occur at a last-mile edge router by judiciously discarding packets that contain frame blocks less important to the analytics at the destination. To achieve this, we first devise a frame-block-aware RTP header extension to effectively decouple packet dependencies to encode frame blocks. Second, Lizard uses a priority-based feedback mechanism that dynamically evaluates packet priorities based on relative accuracy impacts. Third, we develop an adaptive phase-transition-based packet discarding strategy at the router to discard packets that represent unimportant blocks. Our evaluation of Lizard shows improvements over existing methods are substantial: 53.2% reduction in latency and 27.1% increase in analysis accuracy.

cs.NI

Flux: Optimal Scheduling of Optical Circuit Switches for LLM Training

Optical Circuit Switching (OCS) offers high bandwidth density and energy efficiency for LLM training, but incurs a non-negligible reconfiguration delay. Prior work typically schedules optical circuit switches independently of compute, using aggregate traffic demand to determine which circuits to provision and when. We argue that this separation creates a fundamental inefficiency: reconfigurations that ignore the compute timeline can stall communication, resulting in low circuit utilization and large buffer requirements. In this paper, we present Flux, a scheduler that optimally schedules optical circuit switches based on the structure of the entire workload. Flux remains effective across a wide range of switching speeds by reusing circuits and amortizing reconfiguration delay behind compute and communication. We show that Flux reduces training iteration time by up to $10\times$ and peak NIC buffer requirements by more than three orders of magnitude compared to traditional periodic schedulers.

cs.NI